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arXiv:2610.39784

Non-Asymptotic Bounds via the Nussbaum-Szkoła Mapping for Quantum Hypothesis Testing

Jorge Lizarribar-Carrillo, Gonzalo Vazquez-Vilar, Tobias Koch

quant-phSubmitted Oct 29, 2026• Announced Oct 30, 2026• 0 citations
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Abstract

Quantum hypothesis testing concerns the discrimination between quantum states. This paper introduces novel lower and upper bounds for asymmetric quantum hypothesis testing based on the Nussbaum-Szkoła mapping. Together, these bounds provide a unified recovery of both converse and achievability results across all major asymptotic regimes, including large-, moderate-, and small-deviations. Unlike existing bounds, which either rely on technically involved information-spectrum arguments or suffer from fixed prefactors and limited applicability in the non-asymptotic regime, the proposed bounds arise from a unified framework and enable, in some cases, the direct use of classical results. It is further demonstrated that these bounds provide accurate approximations to the optimal quantum error trade-off function at small blocklengths. Numerical comparisons with existing bounds, including those based on fidelity and information spectrum methods, highlight their improved tightness.

AI Summary

The paper studies non-asymptotic bounds on the optimal type-II error $\beta_\alpha(\rho,\sigma)$ under a type-I error constraint $\alpha$, using the classical Nussbaum–Szkoła distributions $P,Q$ associated with the states’ spectral decompositions. Its main general converse is $\beta_\alpha(\rho,\sigma)\ge s\,\beta_{\alpha/(1-s)}(P,Q)$ for $0\le s<1$ and $\alpha\le1-s$. The proof combines the variational characterization of the quantum testing trade-off with a weighted Cauchy–Schwarz inequality. Together with the existing achievability bound $\beta_\alpha(\rho,\sigma)\le s^{-1}\beta_{(1-s)\alpha}(P,Q)$, this gives a classical–quantum comparison with adjustable error rescalings and prefactors. The converse is shown to be asymptotically tight for nearly orthogonal pure states. When $\rho$ is pure, a stronger achievability bound is established: $\beta_\alpha(\rho,\sigma)\le s_1^{-1}\beta_{s_2\alpha}(P,Q)$, with $s_1\le1$ for $s_2\le1/2$ and $s_1\le4s_2(1-s_2)$ otherwise. A rank-one perturbation analysis yields this region and a sharp universal factor $4/3$ comparing the quantum weighted minimum with its Nussbaum–Szkoła counterpart in the pure–mixed setting. Choosing blocklength-dependent parameters recovers known second-order asymptotics with $O(\log n)$ remainder, moderate-deviation expansions, and Hoeffding exponents from classical results. Numerical comparisons use five-copy qubit examples. The paper notes that existing harmonic-mean bounds are tighter in the mixed–mixed examples and recover the exact third-order term, whereas the proposed bounds emphasize analytic tractability. The improved achievability bound requires a pure null state; the asymptotic analysis assumes $\operatorname{supp}(\rho)\subseteq\operatorname{supp}(\sigma)$.

Some signs of AI-generated textIncrementalPossible errorUnclear writing

Error found: The moderate-deviations converse incorrectly substitutes the signed third cumulant for the absolute third central moment required by Rozovsky's lemma, invalidating that application as written. The printed Tomamichel–Hayashi achievability benchmark gives an upper bound of 1/4 when rho=sigma=I/2 and alpha=1/4, despite the exact optimum being 3/4. The appendix reverses both comparisons between 4s2(1−s2) and 2s2, although correcting the branch analysis recovers the parameter region stated in Lemma 3.

Written by gpt-6.1-sol from the paper's full text. Automated: it can be wrong.

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Quantum hypothesis testing concerns the discrimination between quantum states. This paper introduces novel lower and upper bounds for asymmetric quantum hypothesis testing based on the Nussbaum-Szkoła mapping. Together, these bounds provide a unified recovery of both converse and achievability results across all major asymptotic regimes, including large-, moderate-, and small-deviations. Unlike existing bounds, which either rely on technically involved information-spectrum arguments or suffer from fixed prefactors and limited applicability in the non-asymptotic regime, the proposed bounds arise from a unified framework and enable, in some cases, the direct use of classical results. It is further demonstrated that these bounds provide accurate approximations to the optimal quantum error trade-off function at small blocklengths. Numerical comparisons with existing bounds, including those based on fidelity and information spectrum methods, highlight their improved tightness.
The paper studies non-asymptotic bounds on the optimal type-II error βα(ρ,σ)\beta_\alpha(\rho,\sigma)βα​(ρ,σ) under a type-I error constraint α\alphaα, using the classical Nussbaum–Szkoła distributions P,QP,QP,Q associated with the states’ spectral decompositions.
Its main general converse is βα(ρ,σ)≥s βα/(1−s)(P,Q)\beta_\alpha(\rho,\sigma)\ge s\,\beta_{\alpha/(1-s)}(P,Q)βα​(ρ,σ)≥sβα/(1−s)​(P,Q) for 0≤s<10\le s<10≤s<1 and α≤1−s\alpha\le1-sα≤1−s. The proof combines the variational characterization of the quantum testing trade-off with a weighted Cauchy–Schwarz inequality. Together with the existing achievability bound βα(ρ,σ)≤s−1β(1−s)α(P,Q)\beta_\alpha(\rho,\sigma)\le s^{-1}\beta_{(1-s)\alpha}(P,Q)βα​(ρ,σ)≤s−1β(1−s)α​(P,Q), this gives a classical–quantum comparison with adjustable error rescalings and prefactors. The converse is shown to be asymptotically tight for nearly orthogonal pure states.
When ρ\rhoρ is pure, a stronger achievability bound is established: βα(ρ,σ)≤s1−1βs2α(P,Q)\beta_\alpha(\rho,\sigma)\le s_1^{-1}\beta_{s_2\alpha}(P,Q)βα​(ρ,σ)≤s1−1​βs2​α​(P,Q), with s1≤1s_1\le1s1​≤1 for s2≤1/2s_2\le1/2s2​≤1/2 and s1≤4s2(1−s2)s_1\le4s_2(1-s_2)s1​≤4s2​(1−s2​) otherwise. A rank-one perturbation analysis yields this region and a sharp universal factor 4/34/34/3 comparing the quantum weighted minimum with its Nussbaum–Szkoła counterpart in the pure–mixed setting.
Choosing blocklength-dependent parameters recovers known second-order asymptotics with O(log⁡n)O(\log n)O(logn) remainder, moderate-deviation expansions, and Hoeffding exponents from classical results. Numerical comparisons use five-copy qubit examples. The paper notes that existing harmonic-mean bounds are tighter in the mixed–mixed examples and recover the exact third-order term, whereas the proposed bounds emphasize analytic tractability. The improved achievability bound requires a pure null state; the asymptotic analysis assumes supp⁡(ρ)⊆supp⁡(σ)\operatorname{supp}(\rho)\subseteq\operatorname{supp}(\sigma)supp(ρ)⊆supp(σ).
Quantum hypothesis testing concerns the discrimination between quantum states. This paper introduces a novel lower bound for asymmetric quantum hypothesis testing that is based on the Nussbaum-Szkoła mapping. The lower bound provides a unified recovery of converse results across all major asymptotic regimes, including large-, moderate-, and small-deviations. Unlike existing bounds, which either rely on technically involved information-spectrum arguments or suffer from fixed prefactors and limited applicability in the non-asymptotic regime, the proposed bound arises from a single expression and enables, in some cases, the direct use of classical results. It is further demonstrated that the proposed bound provides accurate approximations to the optimal quantum error trade-off function at small blocklengths. Numerical comparisons with existing bounds, including those based on fidelity and information spectrum methods, highlight its improved tightness.
We consider Bayesian discrimination among multiple quantum states and establish a dimension-free one-shot upper bound on the minimum probability of error in terms of the sum of pairwise errors. This resolves a conjecture of Audenaert and Mosonyi [J. Math. Phys. 55 (2014)] and improves the multiple quantum Chernoff bound of Li [Ann. Statist. 44 (2016)] by removing its dimension-dependent prefactor. In the asymptotic many-copy regime, our bound proves the achievability of the multiple quantum Chernoff distance for arbitrary separable Hilbert spaces, thereby settling the previously open infinite-dimensional case, and further yields constant-factor sharp asymptotics for the optimal error probability. In binary quantum hypothesis testing, we prove that the minimum error probability is characterized, up to universal constants, by a trace harmonic-mean quantity. Consequently, the optimal binary quantum error probability is within a factor of two of the optimal classical error probability for the associated Nussbaum-Szkoła distributions, complementing the lower bound of Nussbaum and Szkoła [Ann. Statist. 37 (2009)].
The ultimate limits of quantum state discrimination are often thought to be captured by asymptotic bounds that restrict the achievable error probabilities, notably the quantum Chernoff and Hoeffding bounds. Here we study hypothesis testing protocols that are permitted a probability of producing an inconclusive discrimination outcome, and investigate their performance when this probability is suitably constrained. We show that even by allowing an arbitrarily small probability of inconclusiveness, the limits imposed by the quantum Hoeffding and Chernoff bounds can be significantly exceeded. This completely circumvents the conventional trade-offs between error exponents in hypothesis testing while incurring only a vanishingly small overhead over conventional approaches. Such improvements over standard state discrimination are robust and can be obtained even when an exponentially vanishing probability of inconclusive outcomes is demanded. Relaxing the constraints on the inconclusive probability can enable even larger advantages, but this comes at a price. We show a 'strong converse' property of this setting: targeting error exponents beyond those achievable with vanishing inconclusiveness necessarily forces the probability of inconclusive outcomes to converge to one. By exactly quantifying the rate of this convergence, we give a complete characterisation of the trade-offs between error exponents and rates of conclusive outcome probabilities. Overall, our results provide a comprehensive asymptotic picture of how the allowance for inconclusive measurement outcomes reshapes optimal quantum hypothesis testing.
Two types of errors can occur when discriminating pairs of quantum states. Asymmetric state discrimination involves minimizing the probability of one type of error, subject to a constraint on the other. We give explicit expressions bounding the set of achievable errors, using the trace norm, the fidelity, and the quantum Chernoff bound. The upper bound is asymptotically tight and the lower bound is exact for pure states. Unlike asymptotic bounds, our bounds give error values instead of exponents, so can give more precise results when applied to finite-copy state discrimination problems.
Hypothesis exclusion is an information-theoretic task in which an experimenter aims at ruling out a false hypothesis from a finite set of known candidates, and an error occurs if and only if the hypothesis being ruled out is the ground truth. For the tasks of quantum state exclusion and quantum channel exclusion - where hypotheses are represented by quantum states and quantum channels, respectively - efficiently computable upper bounds on the asymptotic error exponents were established in a recent work of the current authors [Ji et al., arXiv:2407.13728 (2024)], where the derivation was based on nonasymptotic analysis. In this companion paper of our previous work, we provide alternative proofs for the same upper bounds on the asymptotic error exponents of quantum state and channel exclusion, but using a conceptually different approach from the one adopted in the previous work. Specifically, we apply strong converse results for asymmetric binary hypothesis testing to distinguishing an arbitrary “dummy” hypothesis from each of the concerned candidates. This leads to the desired upper bounds in terms of divergence radii via a geometrically inspired argument.
Sequential methods for quantum hypothesis testing offer significant advantages over fixed-length approaches, which rely on a predefined number of state copies. Despite their potential, these methods remain underexplored for unambiguous discrimination. In this work, we derive performance bounds for such methods when applied to the discrimination of a set of pure states. The performance is evaluated based on the expected number of copies required. We establish a lower bound applicable to any sequential method and an upper bound on the optimal sequential method. The upper bound is derived using a novel and simple non-adaptive method. Importantly, the gap between these bounds is minimal, scaling logarithmically with the number of distinct states.